Making use of the historical daily water supply data of four water plants in Yiwu city, a new water supply prediction model based on long short term memory (LSTM) neural network improved by integrated learning algorithm was proposed, in order to effectively resolve the problems of low accuracy and insufficient generalization ability of the daily water supply prediction. In the model, a historical daily water supply after pre-processing by Pauta criterion was taken as the data input, the LSTM neural network with long-term temporal information memory was applied as the weak predictor of integrated learning, the grid search method was utilized for network hyperparameter tuning, and the AdaBoost integrated learning algorithm was used to weight the combination of the weak predictors to obtain the strong predictor. Results show that the improved LSTM neural network based on integrated learning algorithm has the highest Nash efficiency coefficient (NSE) with the lowest root mean square error (RMSE) and mean absolute error (MAE), the best fitting effect on the change trend and the peak value of daily water supply data, compared with the random forest (RF), AdaBoost and LSTM neural network. The time series prediction accuracy of the improved LSTM water supply forecasting model is significantly improved, with good generalization ability and stable prediction performance. The results can provide an important reference for the rational allocation of urban water resources planning and integrated intelligent water supply scheduling.